In this episode, we explore how PayPal estimates incremental lift in customer value using synthetic control methods. This causal inference–based approach provides a principled way to construct a counterfactual and isolate causal effects when traditional experiments aren’t sufficient, helping teams measure true impact in a complex, noisy, real-world environment and make more informed decisions.

For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/paypal-tech/estimating-incremental-lift-in-customer-value-delta-cv-using-synthetic-control-522be5e3da3a

In this episode, we explore how Netflix tackles the challenge of predicting user session intent by extending the capabilities of its foundation model with a hierarchical multi-task learning architecture. This approach helps Netflix better understand what users want in the moment and personalize the experience in real time, ultimately improving its recommendation system at scale.

For more details, you can refer to their published tech blog, linked here for your reference: https://netflixtechblog.com/fm-intent-predicting-user-session-intent-with-hierarchical-multi-task-learning-94c75e18f4b8

In this episode, we explore how CVS Health builds its product recommendation system to deliver relevant, timely suggestions across millions of customers and thousands of products. We look at the business motivation behind personalization at CVS, and then walk through how the team uses Word2Vec, Euclidean distance, LLM-generated product summaries, and iterative refinement to improve the system step by step.

For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/cvs-health-tech-blog/enhancing-you-may-also-like-ymal-systems-using-llms-and-word2vec-0340280019d2